store = {}
store['args']={'name': 'emnist_independent_bald_k10_706460', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 706460, 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 4, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=4', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.023351063829787234, 'nll': 3.8639504609209427}, 'chosen_samples': [58178, 82910, 26801, 86941, 17638], 'chosen_samples_score': [0.02562763020396197, 0.025861269235610607, 0.026576438918709755, 0.02592223659157744, 0.026007970422506244], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.051542553191489364, 'nll': 73.42008693289249}, 'chosen_samples': [4856, 98301, 68870, 7171, 88811], 'chosen_samples_score': [1.3214472426705677, 1.3263047528813925, 1.3304863693198308, 1.3405258994760942, 1.342505717234043], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.08117021276595744, 'nll': 50.02028247589761}, 'chosen_samples': [74304, 76582, 89285, 11014, 101886], 'chosen_samples_score': [1.3993979632125186, 1.4057815203873076, 1.4362649488310248, 1.4230872584443528, 1.4311301618205188], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.09553191489361702, 'nll': 32.722628475757354}, 'chosen_samples': [55355, 62646, 20573, 108503, 16181], 'chosen_samples_score': [1.408443049923288, 1.4159687134120729, 1.4312399214510714, 1.456170598844595, 1.499287491191808], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.12186170212765958, 'nll': 26.071501430754967}, 'chosen_samples': [12301, 885, 53987, 76395, 9517], 'chosen_samples_score': [1.5212343673760103, 1.5263682215782588, 1.5398493077504831, 1.5516660505955857, 1.5491621735491572], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1477127659574468, 'nll': 32.34208768154713}, 'chosen_samples': [52353, 39856, 112626, 95437, 45467], 'chosen_samples_score': [1.513529868189476, 1.5228839289312175, 1.5396209880613958, 1.547327330113979, 1.561687908134234], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1451063829787234, 'nll': 27.90722711603692}, 'chosen_samples': [58390, 73773, 14178, 75936, 3565], 'chosen_samples_score': [1.4710070735656415, 1.4768464462843296, 1.4781539981170355, 1.4867710507343936, 1.5111125012995612], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.15845744680851065, 'nll': 26.82293089197037}, 'chosen_samples': [109099, 44898, 37277, 109617, 48123], 'chosen_samples_score': [1.6125627348802376, 1.614061020058645, 1.6562343911756061, 1.6461149099042491, 1.635590103954045], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.16138297872340426, 'nll': 21.030114018866357}, 'chosen_samples': [80038, 54055, 67922, 105585, 65252], 'chosen_samples_score': [1.7252640101384111, 1.727239412265237, 1.7658974362953113, 1.8750109513677624, 1.8000207033201854], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.19159574468085105, 'nll': 21.945900046165953}, 'chosen_samples': [27009, 57130, 90412, 24846, 81939], 'chosen_samples_score': [1.6797138879752058, 1.6847103378514876, 1.7097710310508099, 1.7413479299961443, 1.7398927838323262], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.18601063829787234, 'nll': 20.169481079426216}, 'chosen_samples': [41648, 55448, 44832, 89348, 12070], 'chosen_samples_score': [1.6674986422365847, 1.700760280956201, 1.6954600424692297, 1.7420988481023407, 1.8066855982661127], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.20170212765957446, 'nll': 20.307468355868725}, 'chosen_samples': [30146, 85675, 74113, 39166, 19802], 'chosen_samples_score': [1.6593157660713516, 1.665668508003887, 1.669893013428055, 1.6774051417811058, 1.7027337657351658], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.20872340425531916, 'nll': 19.402438141843106}, 'chosen_samples': [93986, 33171, 84530, 78273, 95870], 'chosen_samples_score': [1.481785861055155, 1.5171555825763565, 1.4931644598581086, 1.5290727569016518, 1.5380703221944638], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.19335106382978723, 'nll': 18.765814006074947}, 'chosen_samples': [73353, 69146, 66549, 30109, 98662], 'chosen_samples_score': [1.5608555839750398, 1.5634695407126808, 1.5788228473290817, 1.5906953055006712, 1.6040668066689379], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22537234042553192, 'nll': 18.131732208576608}, 'chosen_samples': [65979, 39219, 25138, 59999, 112219], 'chosen_samples_score': [1.6406830422510068, 1.8226686253856383, 1.6631617356052242, 1.6746060806658616, 1.697277680915648], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.21946808510638297, 'nll': 16.623884770007844}, 'chosen_samples': [90566, 95689, 55568, 49095, 13945], 'chosen_samples_score': [1.6749881666282835, 1.6764437403697579, 1.7280388224433816, 1.824149469988649, 1.7079033541269377], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.24829787234042552, 'nll': 16.96903602600098}, 'chosen_samples': [98841, 23064, 65788, 101239, 34229], 'chosen_samples_score': [1.7575013850121954, 1.7657269216813791, 1.7822939778053197, 1.776708759677447, 1.789468126335337], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.24622340425531916, 'nll': 15.869404826062791}, 'chosen_samples': [77239, 16915, 32463, 35608, 36829], 'chosen_samples_score': [1.6170980677647753, 1.6180109839548193, 1.649413006105564, 1.6351639578493866, 1.6758278728959133], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26643617021276594, 'nll': 15.337842388558895}, 'chosen_samples': [7362, 13865, 7028, 55269, 87603], 'chosen_samples_score': [1.7123187841424619, 1.7291442211874468, 1.7318752944278257, 1.740445768394494, 1.7878270561339271], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2621276595744681, 'nll': 15.792206136825238}, 'chosen_samples': [65074, 86409, 92210, 79489, 55794], 'chosen_samples_score': [1.5880384884807992, 1.5923690275032103, 1.650804088949728, 1.688810137540361, 1.6810712821925493], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2776063829787234, 'nll': 13.968429858430904}, 'chosen_samples': [59100, 55003, 112313, 15349, 57837], 'chosen_samples_score': [1.5966202749899576, 1.6016689455944673, 1.7061493992393646, 1.6290522829704752, 1.664347755073281], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.273031914893617, 'nll': 13.534215884107224}, 'chosen_samples': [95270, 45645, 79347, 97533, 2790], 'chosen_samples_score': [1.6784164463990634, 1.6796343716654851, 1.7143928997118127, 1.7414300335599813, 1.7625686932565428], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.3057446808510638, 'nll': 13.905134925842285}, 'chosen_samples': [100972, 76245, 52, 18894, 65327], 'chosen_samples_score': [1.6468440690962867, 1.6527668011634562, 1.6651792857716923, 1.6697910895968966, 1.6824900904706117], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3018085106382979, 'nll': 12.045552867321257}, 'chosen_samples': [35593, 37883, 26048, 36080, 59818], 'chosen_samples_score': [1.749238859598869, 1.776226393850762, 1.8247756048341233, 1.849932378376317, 1.8010037369155847], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2951063829787234, 'nll': 13.874106116599226}, 'chosen_samples': [66499, 102053, 94515, 11522, 94539], 'chosen_samples_score': [1.8209492012826922, 1.8759689728939155, 1.833181732919027, 1.8417550620266638, 1.84617522624083], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.30686170212765956, 'nll': 12.365585748388412}, 'chosen_samples': [24550, 63728, 13236, 14409, 61914], 'chosen_samples_score': [1.7130466670455498, 1.7334965771814574, 1.7694767337559703, 1.7872259304003348, 1.8250667461872303], 'chosen_samples_orignal_score': None})
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